Applied Predictive Modeling

By Max Kuhn and Kjell Johnson

The back cover blurb:

This text is intended for a broad audience as both an introduction to predictive models as well as a guide to applying them. Non-mathematical readers will appreciate the intuitive explanations of the techniques while an emphasis on problem-solving with real data across a wide variety of applications will aid practitioners who wish to extend their expertise. Readers should have knowledge of basic statistical ideas, such as correlation and linear regression analysis. While the text is biased against complex equations, a mathematical background is needed for advanced topics.

We noticed that most machine learning books are focused either on the theoretical descriptions of models or are software manuals. Our book attempts to:

give an intuitive description of models,

illustrate the practical aspects of training them and

provide software and data sets so that readers can reproduce our work.